THE URINE PROTEIN TO CREATININE RATIO (P/C) AS A PREDICTOR OF 24-HOUR URINE PROTEIN EXCRETION IN RENAL TRANSPLANT PATIENTS
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to examine the utility of the random urine protein to creatinine ratio (P/C) in evaluation and longitudinal management of proteinuria in adult renal transplant recipients with or without overt nephropathy in an outpatient clinic. METHODS: A total of 289 adult renal transplant recipients provided 24-hr urine collections for total protein and creatinine, followed by a random urine for protein and creatinine. For longitudinal analysis, 192 of these patients provided two 24-hr urine collections with concomitant random urine specimens separated on average by 6.8 months. As well, 134 patients provided a total of 851 multiple-paired spot and 24-hr urine samples (range 2 to 12) over a 2-year period. RESULTS: The log random urine P/C ratio correlated significantly to the log 24 UP (r=0.749, P<0.0001) with or without nephrotic range proteinuria. High sensitivity (74.4-90%) and specificity values (93-98%) were found for estimating proteinuria from 0.5 to 2 g/day. However, the precision of estimation decreased as the level of urinary protein excretion increased to >3 g/day. The positive predictive value decreased as proteinuria became >3 g/day, perhaps because of the low prevalence of patients with high level proteinuria in our sample. The direction of change in P/C ratio longitudinally was accompanied by a similar direction of change in 24 UP, which was highly significant (r=0.7555, P<0.0001). CONCLUSION: We conclude that the urine P/C ratio is a useful and convenient screening and longitudinal test for proteinuria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".